LECTURE #9: FUZZY LOGIC & NEURAL NETS
Monday March 1, 2004
DSES-4810-01 Intro to COMPUTATIONAL INTELLIGENCE
& SOFT COMPUTING
Instructor: Prof. Mark J. Embrechts (x 4009 or 371-4562) (embrem@rpi.edu)
Office Hours: Thursday 10-11 am (CII5217), or by appointment.
Class Time: Monday/Thursday: 8:30-9:50 (Amos eaton 216)
TEXT: J. S. Jang, C. T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing,” Prentice Hall, 1996. (1998) ISBN 0-13-261066-3
LECTURES #12&13: Direct Kernel Methods
This lecture will introduce the paradigm of direct kernel methods, which reconciles neural networks, statistical multivariate regression and support vector machines in a single framework. Direct kernel methods assume a kernel transform as a data preprocessing step, rather than an inherent part of the learning method. By applying a direct kernel transform, traditional methods such as Principal Component Analysis (PCA), Ridge Regression, Partial Least Squares (PLS), Independent Component Analysis (ICA) or simple one-layered neural networks, can be transformed into powerful nonlinear modeling and machine learning tools.
This presentation will highlight several industrial applications of direct kernel methods such as network intrusion detection, the detection of ischemia from magnetocariograms, in-silico drug design, the electronic nose, gene expression arrays, and the detection of mixtures of chemical substances from spectral data.
Handouts
1. Lecture Slides posted on website
2. Mark J. Embrechts, “Direct Kernel Least-Squares Support Vector Machines with Heuristic Regularization,” Submitted for presentation to IJCNN2004, Budapest, July 2004.
Deadlines
January 22 Homework Problem #0 (web browsing)
January 29 Project Proposal
February 17 Homework Problem #1 (paper)
February 19 Quiz #1 (20 minutes, Chapter 2 of Book)
February 23 Homework Problem #2 (peaks function)
March 16 Progress Report
April 8 No Class
April 22/April 26 Final Presentations
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